CairnCareers
DashboardFree for a year after launch
Pay and growth in context

Money is context. It never moves the score.

Salary, job growth, and location sit beside the AI-exposure number so you can weigh a path properly. They are display only, and by design they cannot shift the exposure figure by a single point.

Sample data. Every figure below is illustrative, not a real wage estimate.
Median pay where you would actually work

A national median hides two things that change the number a lot: the metro you are in, and whether the role sits in an office.

Cambridge, Massachusetts
Fixed to the sample ZIP code. Entering your own turns on when the wage engine is connected at launch.
How you would work
Adjusted median
$103,800
UX Researcher2
National median$92,700
Metro index1.12
Applied at100 percent, in office

Remote roles are priced closer to the national band, so the metro premium applies only in part. Move the toggle and watch it.

Careers worth weighing

Four paths your evidence already reaches

Target role

UX Researcher

$92,7002
16 percent growth to 20342
AI already does about 54 percent1
Close match

Product Designer

$96,4002
3 percent growth to 20342
AI already does about 49 percent1
Close match

Market Research Analyst

$74,3002
8 percent growth to 20342
AI already does about 61 percent1
Close match

Behavioral Data Analyst

$83,1002
11 percent growth to 20342
AI already does about 66 percent1
The wall

Pay data cannot reach the score

The exposure number on this page is built from O*NET task data, Eloundou et al., and METR*, and from nothing else. Wages, growth projections, your ZIP code, your network, and your portfolio are all shown beside it and none of them can move it. That separation is the reason the number means anything. A 0–100 exposure scale.

Method and sources

  1. O*NET OnLine, occupational task data, with Eloundou et al. (2024), Science 384:1306-1308 and METR, long-run time-horizon trend*
  2. U.S. Bureau of Labor Statistics, Occupational Outlook Handbook and U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics. Metro and work-condition adjustment is a modelled illustration for this sample, not a published BLS figure.

The published AI-exposure number is built only from O*NET task data, Eloundou et al., and METR*. Pay, growth, networking, portfolio, and clean-up are context and never move it. A 0–100 exposure scale.

* This projection uses METR's established long-run time-horizon trend, not the faster 2026 estimates. Those newer figures come from a task suite near saturation, carry very wide confidence intervals, and measure software tasks specifically, so extrapolating them across all occupations is not yet reliable. See MIT Technology Review, "This is the most misunderstood graph in AI," February 5, 2026, https://www.technologyreview.com/2026/02/05/1132254/this-is-the-most-misunderstood-graph-in-ai/

This dashboard is a sample. Maya Rivera, Northlight University, Northbeam, Lumen, and Verdi Labs are invented, and every figure on this page is sample data, not a real result.

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